{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Author of code: Stiene Praet\n",
    "- Date: 01/05/2021\n",
    "- Purpose: Draw the figures and Tables for the US2016_FB_likes paper\n",
    "- Data IN: User likes data \n",
    "           excel files with the homogeneity and ideology scores for the pages and users \n",
    "           (i.e. the output of Calculate_ideology_entropy.ipynb)\n",
    "- Data OUT: Figures and Tables of results Section\n",
    "            csv file with densities to calculate overlap in R\n",
    "            csv file with user data for beta regressions in R\n",
    "- Machine: local"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ast\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "!pip install chart_studio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chart_studio\n",
    "chart_studio.tools.set_credentials_file(username='stiene.praet', api_key='9X96akVZKJcSaiNtCrYA')\n",
    "chart_studio.tools.set_config_file(world_readable=True,\n",
    "                             sharing='public')\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:583: FutureWarning:\n",
      "\n",
      "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "pages_info = pd.read_excel('../data/pages_info_coded_.xlsx') # load pages data\n",
    "user_info = pd.read_excel('../data/user_info.xlsx') # load user data\n",
    "user_likes = pd.read_csv('../data/user_likes.csv', encoding='UTF-8', index_col=0) # load likes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge user likes with pages info\n",
    "user_likes2 =user_likes.merge(pages_info[['page_name','cramer_v','page_ideo_corr','group']], on='page_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select liberal, moderate and conservative users\n",
    "lib_users = user_info[user_info['ideo5']<3]['resp_id']\n",
    "mod_users = user_info[user_info['ideo5']==3]['resp_id']\n",
    "con_users = user_info[user_info['ideo5'].isin([4,5])]['resp_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1 Page homogeneity accross categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# page ideology distribution (Figure 5)\n",
    "density = pd.DataFrame()\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    group = []\n",
    "\n",
    "    lib = user_likes2[(user_likes2['resp_id'].isin(lib_users))&(user_likes2['group'].str.contains(gr))]['page_ideo_corr'].tolist()\n",
    "    mod = user_likes2[user_likes2['resp_id'].isin(mod_users)&(user_likes2['group'].str.contains(gr))]['page_ideo_corr'].tolist()\n",
    "    con = user_likes2[user_likes2['resp_id'].isin(con_users)&(user_likes2['group'].str.contains(gr))]['page_ideo_corr'].tolist()\n",
    "    group.append(lib)\n",
    "    group.append(mod)\n",
    "    group.append(con)\n",
    "    density[gr] = group\n",
    "    \n",
    "    plt.figure()\n",
    "    sns.distplot(lib, hist=False, kde_kws = {'shade': True, 'linewidth': 1}, color='b', kde=True)\n",
    "    sns.distplot(mod, hist=False, kde_kws = {'shade': True, 'linewidth': 1}, color='purple',  kde=True)\n",
    "    sns.distplot(con, hist=False, kde_kws = {'shade': True, 'linewidth': 1}, color='r',  kde=True)\n",
    "    plt.xlabel('Page ideology')\n",
    "    plt.ylabel('Density')\n",
    "    plt.xlim(0,1)\n",
    "    plt.ylim(0,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save densities and calculate OVL in R\n",
    "density.to_csv('../data/density.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# page homogeneity distribution (Figure 6)\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    group = pages_info[(pages_info['group'].str.contains(gr))]\n",
    "    plt.figure()\n",
    "    sns.distplot(group[\"cramer_v\"], hist=False, kde_kws = {'shade': True, 'linewidth': 1}, color='purple', kde=True)\n",
    "    plt.xlabel('Homogeneity score')\n",
    "    plt.ylabel('Density')\n",
    "    plt.xlim(0,1)\n",
    "    plt.ylim(0,18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Page homogeneity and ideology per Facebook category\n",
    "(Table 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load categories and descriptions\n",
    "Cats = pd.read_excel('../data/Facebook_categories.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create list with category names\n",
    "categories = Cats['Category'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define function for weighted average\n",
    "\n",
    "def wavg(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n",
    "    \n",
    "def wavg_and_std(values, weights):\n",
    "    \"\"\"\n",
    "    Return the weighted average and standard deviation.\n",
    "\n",
    "    values, weights -- Numpy ndarrays with the same shape.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        average = np.average(values, weights=weights)\n",
    "        # Fast and numerically precise:\n",
    "        variance = np.average((values-average)**2, weights=weights)\n",
    "    except:\n",
    "        average=np.nan\n",
    "        variance=np.nan\n",
    "    return (average, math.sqrt(variance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate weighted average and standard deviation for homogeneity (cramer_v)\n",
    "# and page ideology for all Facebook categories \n",
    "\n",
    "cramer_v_avg = []\n",
    "cramer_v_std = []\n",
    "ideo_corr_avg =[]\n",
    "ideo_corr_std = []\n",
    "for t in categories:\n",
    "    group = pages_info[pages_info['category'].str.contains(t)]\n",
    "    avg,std = wavg_and_std(group['cramer_v'],group['page_count'])\n",
    "    cramer_v_avg.append(avg)\n",
    "    cramer_v_std.append(std)\n",
    "    avg,std = wavg_and_std(group['page_ideo_corr'],group['page_count'])\n",
    "    ideo_corr_avg.append(avg)\n",
    "    ideo_corr_std.append(std)\n",
    "    \n",
    "group = pages_info\n",
    "avg,std = wavg_and_std(group['cramer_v'],group['page_count'])\n",
    "cramer_v_avg.append(avg)\n",
    "cramer_v_std.append(std)\n",
    "avg,std = wavg_and_std(group['page_ideo_corr'],group['page_count'])\n",
    "ideo_corr_avg.append(avg)\n",
    "ideo_corr_std.append(std)\n",
    "categories.append('Tot')\n",
    "\n",
    "group = pages_info[pages_info['news_politics'] == 'y']\n",
    "avg,std = wavg_and_std(group['cramer_v'],group['page_count'])\n",
    "cramer_v_avg.append(avg)\n",
    "cramer_v_std.append(std)\n",
    "avg,std = wavg_and_std(group['page_ideo_corr'],group['page_count'])\n",
    "ideo_corr_avg.append(avg)\n",
    "ideo_corr_std.append(std)\n",
    "categories.append('Political news')\n",
    "\n",
    "#hardnews\n",
    "group = pages_info[pages_info['news2'] == 'hardnews'] \n",
    "avg,std = wavg_and_std(group['cramer_v'],group['page_count'])\n",
    "cramer_v_avg.append(avg)\n",
    "cramer_v_std.append(std)\n",
    "avg,std = wavg_and_std(group['page_ideo_corr'],group['page_count'])\n",
    "ideo_corr_avg.append(avg)\n",
    "ideo_corr_std.append(std)\n",
    "categories.append('Hardnews')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>cramer_v_avg</th>\n",
       "      <th>cramer_v_std</th>\n",
       "      <th>ideo_corr_avg</th>\n",
       "      <th>ideo_corr_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Government &amp; Politics</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Political news</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hardnews</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Civil Society</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Identity &amp; Religion</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Individual opinion leaders</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>News &amp; Media</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Public Figures</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Arts &amp; Culture</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Tot</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tv Shows</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Entertainment</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Research &amp; Education</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Music</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Interests</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Movies</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sports</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Services</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Beauty &amp; Health</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Travel</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shopping &amp; retail</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food &amp; Beverage</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cars and transportation</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Category  cramer_v_avg  cramer_v_std  ideo_corr_avg  \\\n",
       "7        Government & Politics          0.22          0.09           0.57   \n",
       "21              Political news          0.18          0.10           0.57   \n",
       "22                    Hardnews          0.15          0.10           0.55   \n",
       "11               Civil Society          0.12          0.09           0.51   \n",
       "17         Identity & Religion          0.12          0.05           0.66   \n",
       "18  Individual opinion leaders          0.12          0.11           0.60   \n",
       "5                 News & Media          0.12          0.09           0.54   \n",
       "1               Public Figures          0.10          0.09           0.51   \n",
       "13              Arts & Culture          0.07          0.06           0.48   \n",
       "20                         Tot          0.07          0.07           0.51   \n",
       "4                     Tv Shows          0.07          0.05           0.48   \n",
       "6                Entertainment          0.07          0.05           0.51   \n",
       "19        Research & Education          0.06          0.04           0.46   \n",
       "3                        Music          0.06          0.03           0.50   \n",
       "12                   Interests          0.06          0.03           0.49   \n",
       "8                       Movies          0.06          0.04           0.48   \n",
       "14                      Sports          0.05          0.03           0.50   \n",
       "10                    Services          0.05          0.04           0.51   \n",
       "9              Beauty & Health          0.04          0.02           0.50   \n",
       "16                      Travel          0.04          0.02           0.52   \n",
       "0            Shopping & retail          0.04          0.02           0.51   \n",
       "2              Food & Beverage          0.04          0.02           0.50   \n",
       "15     Cars and transportation          0.04          0.02           0.52   \n",
       "\n",
       "    ideo_corr_std  \n",
       "7            0.25  \n",
       "21           0.23  \n",
       "22           0.20  \n",
       "11           0.19  \n",
       "17           0.13  \n",
       "18           0.17  \n",
       "5            0.17  \n",
       "1            0.16  \n",
       "13           0.13  \n",
       "20           0.11  \n",
       "4            0.11  \n",
       "6            0.10  \n",
       "19           0.09  \n",
       "3            0.10  \n",
       "12           0.10  \n",
       "8            0.10  \n",
       "14           0.08  \n",
       "10           0.06  \n",
       "9            0.05  \n",
       "16           0.06  \n",
       "0            0.05  \n",
       "2            0.05  \n",
       "15           0.06  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 3\n",
    "cats_info = pd.DataFrame(categories, columns={'Category'})\n",
    "cats_info['cramer_v_avg'] = cramer_v_avg\n",
    "cats_info['cramer_v_std'] = cramer_v_std\n",
    "cats_info['ideo_corr_avg'] = ideo_corr_avg\n",
    "cats_info['ideo_corr_std'] = ideo_corr_std\n",
    "cats_info.sort_values('cramer_v_avg', ascending=False).round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative distribution of page likes  (Figure SA4)\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    group = pages_info[(pages_info['group'].str.contains(gr))]\n",
    "    # some fake data\n",
    "    data = group.sort_values('cramer_v')['page_count'].values\n",
    "    base = group.sort_values('cramer_v')['cramer_v'].values\n",
    "    #evaluate the cumulative\n",
    "    cumulative = np.cumsum(data)\n",
    "    # plot the cumulative function\n",
    "    plt.plot(base, cumulative, c='blue')\n",
    "    plt.xlabel(\"Page homogeneity\")\n",
    "    plt.ylabel(\"Cumulative number of likes\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shopping & retail\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plotly.com"
       },
       "data": [
        {
         "marker": {
          "size": [
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          "Disney Store",
          "Cape Cod Potato Chips",
          "Cheetos",
          "Express",
          "EntirelyPets",
          "Share Your Freebies",
          "Milk",
          "Lance Snacks",
          "Enjoy Life Foods",
          "Pirate's Booty",
          "Paper Mate",
          "Subaru of America, Inc.",
          "Liquid - Plumr",
          "Seattle's Best Coffee",
          "Zaycon Fresh",
          "Birds Eye Vegetables",
          "Lord & Taylor",
          "Juicy Couture",
          "McCormick Spice",
          "Revlon",
          "Freschetta",
          "Badass Jewelry",
          "Rosegal",
          "Airheads Candy",
          "Charlotte Russe",
          "Dell",
          "Crystal Light",
          "Honda",
          "Tillamook",
          "Santa Rita Wines",
          "Chuck Berry",
          "Tria Beauty",
          "Fabletics",
          "Mitchell Gold + Bob Williams",
          "Coffee-mate natural bliss",
          "Hungry Howie's Pizza",
          "Saline Soothers",
          "Victoria's Secret PINK",
          "Sunkist Soda",
          "Barbara's Bakery",
          "belVita",
          "Mom Made Foods",
          "Black Label Bacon",
          "Kay Jewelers",
          "Seagate",
          "California Olive Ranch",
          "TriCalm",
          "Wisk",
          "Fisher-Price",
          "Dockers",
          "Tabanero",
          "20 Mule Team Borax",
          "Pillsbury Baking",
          "LAY'S STAX",
          "Natural Balance Pet Foods",
          "Yves Rocher USA",
          "Canon",
          "Gerber",
          "Ethan Allen",
          "Chef Boyardee",
          "HARIBO USA",
          "Banana Republic",
          "Ford Motor Company",
          "Algenist",
          "Duck Tape",
          "Life is Good",
          "FIORA",
          "Pottery Barn",
          "Boar's Head",
          "Effy Jewelry",
          "Lunchables",
          "FabKids.com",
          "Chuao Chocolatier",
          "Stacy's Pita Chips",
          "Scotts Lawn Care",
          "Infusium 23",
          "Bed Head Styling",
          "Bare Necessities",
          "Oral-B",
          "Nature's Recipe",
          "Benefit Cosmetics",
          "TALBOTS",
          "Swiffer",
          "Get It Free",
          "Haier",
          "Jockey",
          "SunRype USA",
          "Trident",
          "Oreck",
          "Scotch-Brite",
          "Cost Plus World Market",
          "eBags",
          "Faultless Starch/Bon Ami Company",
          "Chiquita",
          "Coupons.com",
          "Laura Mercier Cosmetics",
          "Command",
          "Lucky Charms",
          "Merrick Pet Care",
          "The Official Shiseido (USA) Page",
          "Pinnacle Vodka",
          "Bounce",
          "Chevrolet",
          "PEEPS",
          "T.J.Maxx",
          "Hyundai",
          "Petco",
          "Wilton Cake Decorating",
          "Life Savers Mints",
          "LovelySkin.com",
          "Famous Footwear",
          "Pottery Barn Kids",
          "SUPERPRETZEL",
          "Electrolux",
          "Advance Auto Parts",
          "REI",
          "JASÖN",
          "Alberto VO5",
          "Crabtree & Evelyn US and Canada",
          "WIRED",
          "MobStub Daily Deals",
          "HauteLook",
          "Iconery Pick Of The Day",
          "Zebra Pen",
          "Happy Family",
          "Fruit2O",
          "Popsicle",
          "Cool Gear International",
          "Baker Creek Heirloom Seed Company",
          "Propel Water",
          "Mike's Hard Lemonade",
          "BIC Lighter",
          "Petreet",
          "CafePress",
          "JELL-O",
          "Kia Motors America",
          "Gap",
          "Bare Snacks",
          "Butterfinger",
          "Schick Hydro",
          "Boston Market",
          "No nonsense",
          "Jack's Frozen Pizza",
          "Kirkland's",
          "Organizher",
          "Liquor.com",
          "Dr Pepper",
          "Amazon Kindle",
          "Filtrete",
          "Gimbal’s Fine Candies",
          "Spike Seasoning Magics",
          "PlanetShoes.com",
          "Sheetz",
          "Swanson Health Products",
          "Pepsi MAX",
          "Extra Gum",
          "e.l.f. Cosmetics",
          "Tweezerman",
          "Nature's Path Organic Foods",
          "Sisley Paris",
          "Ecover North America",
          "Shop Your Way",
          "Fleischmann's Simply Homemade",
          "Playtex Sport",
          "Mezzetta",
          "DICK'S Sporting Goods",
          "Lenovo",
          "Pep Boys - Manny, Moe & Jack",
          "CocoaVia",
          "Eggo",
          "Microsoft Lumia",
          "Modnique",
          "Kellogg's",
          "Neutrogena",
          "O'Reilly Auto Parts",
          "Sun-Maid Girl",
          "Daily's Cocktails",
          "Samsung Camera",
          "Offers.com",
          "Land O'Frost",
          "UNO",
          "Amazon Video Games",
          "7UP",
          "MELT Organic",
          "Serta Mattress",
          "Samsung TV",
          "Post Shredded Wheat",
          "Bose",
          "Rosetti",
          "OXO",
          "Re-Body",
          "BJ's Wholesale Club",
          "Giveaway Promote",
          "Crock-Pot Slow Cooker",
          "Del Monte Fresh Produce",
          "Great Grains",
          "STP",
          "Pier 1 Imports",
          "FIAT USA",
          "Special K",
          "Amazon.com/Fashion",
          "Spencer's",
          "Mentos",
          "ShopRite",
          "American Musical Supply",
          "Bruegger's Bagels",
          "V8",
          "LUNA",
          "Cacique USA",
          "Chico's",
          "zulily",
          "Bargain Bats",
          "Meijer",
          "Lucktastic",
          "Little Trees",
          "KitchenAid",
          "Maker's Mark",
          "NyQuil & DayQuil",
          "Saks Fifth Avenue",
          "Aeropostale",
          "Fantastic Freebies",
          "Knitting Fever and Euro Yarns",
          "Naturalizer",
          "Specialty Foods Group",
          "Drake's Cakes",
          "Land O'Lakes",
          "Pearle Vision",
          "Dial",
          "Rimmel London US",
          "Toyota USA",
          "Shaw Floors",
          "RO*TEL",
          "Woot",
          "Giveaway Tab",
          "Schwinn Bikes",
          "MaraNatha Nut Butters",
          "French's",
          "Ritani",
          "Cuties",
          "Vitalicious",
          "Coldwater Creek",
          "Ford Giveaways",
          "Kellogg's Family Rewards",
          "The Freebie Source",
          "Anolon",
          "Zicam",
          "Papa Murphy's Pizza",
          "Butterball",
          "dressbarn",
          "GE",
          "Bumble and bumble.",
          "Panel App - Install. Earn Prizes. All Free.",
          "D-Link",
          "Fleischmann's Yeast",
          "Boboli",
          "Corning® Gorilla® Glass",
          "Hickory Farms",
          "Target Baby",
          "Peanut Butter & Co.",
          "Aussie",
          "Laura's Lean Beef",
          "Adore Me",
          "Newegg",
          "Kampgrounds of America, Inc.",
          "Tom's Of Maine",
          "Bath & Body Works",
          "Acymer Skincare",
          "Daisy Sour Cream",
          "Glade",
          "Eddie Bauer",
          "BISSELL",
          "Zevia",
          "Tide Stain Release Challenge",
          "Audible",
          "Bacardi",
          "Target",
          "Perdue Chicken",
          "Tervis",
          "Charming Charlie",
          "Noodles & Company",
          "Goose Creek Candle Company",
          "Grocery Shop For FREE!!",
          "GameStop",
          "Very Jane",
          "OPI",
          "Mountain Dew",
          "Welch's",
          "Snyder's of Hanover",
          "Coupons and Freebies Mom",
          "Holsted Jewelers",
          "Seneca Snacks",
          "Samsung Mobile",
          "Living Proof",
          "Marshalls",
          "Moto",
          "Kretschmar",
          "Snapware",
          "Hills Bros Cappuccino",
          "PetSmart",
          "Wellesse",
          "Olay",
          "Toshiba",
          "The Crunchy Condiment Company",
          "Bealls Florida",
          "Lindt Chocolate USA",
          "Barnes & Noble",
          "Tailgating Challenge",
          "Doggyloot",
          "Rudi's Gluten-Free Bakery",
          "Community Coffee",
          "Pendleton Whisky",
          "Trop50",
          "AmLactin",
          "Otter Pops",
          "NatureSweet",
          "Sinfulcolors Professional",
          "Tinkertoy",
          "ShoeDazzle",
          "Aquafresh",
          "Sun Sweeps",
          "LeVian",
          "Chrysler",
          "Belk",
          "Levi's",
          "Tastykake",
          "Catherines Plus Sizes",
          "Juice Beauty",
          "Triscuit",
          "MaximusCards",
          "Burpee Gardens",
          "Bob's Red Mill Natural Foods",
          "Red Pro",
          "PEEPS AND COMPANY™",
          "The Home Depot",
          "High Sierra, Inc.",
          "Mirenesse Cosmetics",
          "MARY KAY",
          "Essentia Water",
          "ASUS Republic of Gamers",
          "Barielle",
          "A Freebie Empire",
          "Dermstore",
          "Eucerin",
          "BIC Marking",
          "NeilMed Sinus Rinse",
          "GimmieFreebies",
          "BehrPaint",
          "Brach's",
          "JOLLY TIME  Pop Corn",
          "AXE",
          "Citi Trends",
          "Kingsford Charcoal",
          "Flaviar",
          "Slurpee",
          "Elmer's",
          "Five Star",
          "Friskies",
          "Sweet Baby Ray's",
          "Chips Ahoy!",
          "Uncrustables",
          "Sour Patch Kids",
          "Old El Paso",
          "New York & Company",
          "Free Samples",
          "Firehouse Subs",
          "Harris Teeter",
          "Louis Vuitton",
          "DenTek Oral Care",
          "Conair Beauty",
          "Sole Society",
          "Scheels",
          "Gorton's Seafood",
          "Gucci",
          "Pfister",
          "Burberry",
          "Savvy: Savings",
          "tjoos",
          "Mega Bloks",
          "Dove Chocolate",
          "Harvest Snaps",
          "Gildan Online",
          "Rousseau Metal inc.",
          "Pompeian",
          "Sony Mobile",
          "Softcup",
          "The Amazing Flameless Candle",
          "Fry's Electronics",
          "Make Use Of",
          "Rent The Runway",
          "NUK-USA",
          "Social Nature",
          "RM Palmer Company",
          "Kool - Aid",
          "John Frieda US",
          "Lee Jeans",
          "Macy's",
          "Schick Intuition",
          "7-Eleven",
          "Troy-Bilt",
          "uni-ball",
          "Wawa",
          "California Avocados",
          "Cat's Pride® Cat Litter",
          "Rosetta Stone",
          "Reeds Jewelers",
          "Wonderful Halos",
          "Ball Park Brand",
          "ScamFree Samples",
          "Tiny Prints",
          "Keebler",
          "VELVEETA",
          "philosophy",
          "Frigidaire",
          "Farm Rich",
          "Jelly Belly",
          "Clinique",
          "Kellogg's Nutri-Grain",
          "Avery",
          "Hotspex Panel",
          "Torani",
          "Nicorette & NicoDerm CQ",
          "Nutella",
          "Avalon Organics",
          "Zappos.com",
          "Ultimate Coupons",
          "On The Border",
          "STOK Grills",
          "Country Time Lemonade",
          "Little Hug Fruit Barrels",
          "Pentel of America",
          "TUMS",
          "Kroger",
          "Points2Shop",
          "Children's Advil",
          "DeMet's TURTLES",
          "Atkins",
          "Ore-Ida",
          "Colgate",
          "Urgent Care Bumper Repair",
          "Doritos",
          "Degree Women",
          "KRIS Wine",
          "Hornitos Tequila",
          "American Girl",
          "Arnold / Oroweat Sandwich Thins",
          "Bridgestone Tires",
          "Hot Wheels",
          "ERA Detergent",
          "WHATSINTODAY",
          "Mrs. Butterworth's",
          "Edwards Desserts",
          "Tootsie Pops",
          "Icelandic Glacial Water",
          "TGI Fridays Frozen Snacks",
          "World's Best Cat Litter",
          "SunnyD",
          "Perry Ellis",
          "Menage a Trois Wines",
          "34 Degrees",
          "California Walnuts",
          "Now Foods",
          "NatureBox",
          "LOFT",
          "Earth Balance",
          "SmartBones®",
          "ZoomBucks",
          "Berricle.com",
          "Ford Cars",
          "Halo Pets",
          "BlackBerry",
          "PlayStation",
          "Biore Skincare",
          "Purina ONE Cats",
          "Hearts On Fire",
          "Fresh Beginnings",
          "RYOBI POWER TOOLS",
          "Caltrate",
          "The Natural Dentist",
          "IAMS",
          "So Delicious Dairy Free",
          "Ann Taylor",
          "Garnier USA",
          "Aerie",
          "Natural Choice",
          "Maxwells Attic",
          "Munchkin",
          "Gatorade",
          "Three Bridges",
          "Voskos Greek Yogurt",
          "Pepperidge Farm Milano cookies",
          "TruMoo Chocolate Milk",
          "Windows",
          "Nitto Tire",
          "LEGO",
          "TRESemmé",
          "Beyond the Rack",
          "JewelScent",
          "Makeup.com",
          "The Duck Brand",
          "Sally Hansen",
          "Made In Nature",
          "AsSeenOnTV.com",
          "MAC Cosmetics",
          "HUGO BOSS",
          "DollarDays",
          "BeFrugal",
          "WINC",
          "Ronzoni",
          "Tootsie Roll",
          "Church's Chicken",
          "Be Snack Ready",
          "Seamless",
          "Dr. Scholl's Socks",
          "By Nature Pet Foods",
          "Tombstone Pizza",
          "Logitech",
          "Werther's Original US",
          "THINaddictives",
          "Payless ShoeSource",
          "BarkBox",
          "Milani",
          "Bali Intimates",
          "Mohawk Flooring",
          "Heluva Good!",
          "Flo, the Progressive Girl",
          "TERRO",
          "Blue Ice Vodka",
          "Sierra Trading Post",
          "Mission",
          "Pearls Olives",
          "Herbal Essences",
          "Nikkis Freebie Jeebies",
          "Red Bull",
          "Goggles4u Eyeglasses",
          "Kandoo",
          "RadioShack",
          "UNIQLO USA",
          "Big Hunk",
          "Post-it",
          "Outshine Snacks",
          "Zep Commercial",
          "Joico",
          "Dirt Devil Vacuums North America",
          "Melitta USA",
          "Kodak Moments",
          "Digestive Advantage",
          "Rent-A-Center",
          "Dyson",
          "Unwash",
          "Rebel Circus",
          "Honey Maid",
          "SKYY Vodka",
          "Go Bold With Butter",
          "Fancy Feast",
          "Cinnabon",
          "Starbucks Frappuccino",
          "Schick Hydro Silk",
          "Teas' Tea",
          "Murphy USA",
          "DeMet’s FLIPZ",
          "OshKosh B'gosh",
          "TreasureTrooper.com",
          "Learning Express Toys",
          "LuxDelux",
          "Fresh Step litter",
          "Pureology Serious Colour Care",
          "INDOCHINO",
          "Mr. Coffee",
          "RYOBI Outdoor Products",
          "SunChips",
          "Nestle Crunch",
          "Clorox",
          "Snapple",
          "Riders by Lee",
          "Havoline",
          "essie",
          "Meta Wellness",
          "Party City",
          "Palmer's",
          "Etsy",
          "Winloot",
          "KIND Snacks",
          "Elizabeth Arden",
          "L'OCCITANE en Provence",
          "CANIDAE Pet Foods",
          "Ereader News Today",
          "The Popcorn Factory",
          "Nestle Toll House",
          "Dewar's",
          "Kellogg's Frosted Flakes",
          "Kiss My Face",
          "BIC Soleil",
          "VTech Toys",
          "Pink Papaya",
          "Bowflex",
          "Value City Furniture",
          "Hollister Co.",
          "Sally Beauty",
          "Dixie",
          "Coleman U.S.A.",
          "Jack Daniel’s Tennessee Honey",
          "Verragio Engagement Rings and Wedding Bands",
          "Calphalon",
          "Jimmy Dean Sausage",
          "Tai Pei Frozen Asian Food",
          "Dole",
          "Jamba Juice",
          "Sour Jacks",
          "ZAGG",
          "Pendaflex",
          "Zatarain's",
          "Earth's Best",
          "Chex",
          "Blockbuster",
          "Beggin'",
          "Coppertone",
          "Cracker Jack'D",
          "Pepsi",
          "Lime-A-Rita",
          "True Lemon",
          "Beach Camera",
          "Bobbi Brown Cosmetics",
          "ipsy",
          "Quick Shine Floor Finish",
          "Maidenform",
          "GHP Group, Inc.",
          "Benefiber",
          "Mills Fleet Farm",
          "CheckPoints",
          "Wayfair",
          "Starburst",
          "HoneyBaked Ham",
          "Skinny Cow",
          "Edy's",
          "SoBe",
          "NESCAFÉ Dolce Gusto US",
          "Blue Diamond Almonds",
          "Sunsweet",
          "Royal Canin USA",
          "göt2b",
          "Blue Steel",
          "Nylabone",
          "Call of Duty",
          "Quaker Chewy Granola Bars",
          "Get Olympus",
          "Reynolds Kitchens",
          "Shoe Carnival",
          "Krusteaz",
          "Armour",
          "Bimbo USA",
          "Thermador",
          "Gameloft",
          "American Lamb Board",
          "Mist Twst",
          "Sweet'N Low",
          "GiftHulk",
          "Dial For Men",
          "Moen",
          "Toyo Tires",
          "Nordic Naturals",
          "Café Pilon",
          "NIVEA MEN",
          "Road Runner Sports",
          "Hartz",
          "Centrum",
          "SEALY MATTRESS",
          "Sunbelt Bakery",
          "Herr's",
          "Durex USA",
          "MUSSELMAN'S",
          "Jack Daniel's Tennessee Whiskey",
          "ALEX Toys",
          "Stonefire Authentic Flatbreads",
          "Banana Boat",
          "A.1. Original Sauce",
          "Kettle Brand",
          "Sleep Number",
          "Tradewinds",
          "My Free Product Samples",
          "AZO",
          "Scotch",
          "FamilySavings",
          "Epicurious",
          "Mercedes-Benz USA",
          "The Sharper Image",
          "Scrabble",
          "Ibotta",
          "Prevacid24HR",
          "MovieTickets.com",
          "al fresco All Natural Chicken Sausage",
          "COVERGIRL",
          "HERSHEY'S",
          "fullbeauty.com",
          "Lay's",
          "Clif Bar",
          "Johnsonville",
          "Helzberg Diamonds",
          "ZonePerfect",
          "Simple Green",
          "Costco",
          "Beauty Collection",
          "3 Musketeers",
          "TWIZZLERS",
          "Blue Nile",
          "Purell",
          "Hanes Hosiery",
          "PetFoodDirect",
          "Curel US",
          "Truvia",
          "Sony Mobile US",
          "Country Crock",
          "LALICIOUS",
          "Baggallini",
          "Red Gold Tomatoes",
          "Free Samples And Freebies",
          "Rack Room Shoes",
          "Tattoos",
          "Pert Plus",
          "MySavings.com",
          "Ebates",
          "Schwarzkopf",
          "Toys''R''Us",
          "Pine Mountain® Firelogs",
          "Pretzel Crisps",
          "Oregon Chai",
          "Popcorn, Indiana",
          "Whirlpool USA",
          "Combos",
          "Tea Collection",
          "Head and Shoulders",
          "Chicago Steak Company",
          "Michael Stars",
          "PotsandPans.com",
          "Hello, Cereal Lovers.",
          "Revlon Hair Tools",
          "iSatori",
          "Diamond Pet Foods",
          "GasBuddy",
          "Progresso",
          "Pure Silk",
          "Barilla",
          "WD-40",
          "Comfort Research",
          "Official Luzianne Tea",
          "One Step Ahead",
          "Grape-Nuts",
          "iHeartCats.com",
          "DealsPlus",
          "Glad",
          "Remington Ready",
          "Hot Topic",
          "CHANEL",
          "Sargento Cheese",
          "Microsoft Edge",
          "DailySale.com",
          "Victoria's Secret",
          "Great American Cookies",
          "Old Wisconsin",
          "Little Tikes",
          "Coupon Mom",
          "Captain Morgan",
          "Influenster",
          "Diamonds Factory",
          "Lean Cuisine",
          "True Religion Brand Jeans",
          "Milk-Bone",
          "Ruffies Trash Bags",
          "Philadelphia Cream Cheese",
          "TeenFreeway",
          "Staub USA",
          "RepHresh Products",
          "Naturally Fresh Deodorant Crystal",
          "Simple Skincare",
          "True Chews",
          "NCIX",
          "Magnum",
          "Yogi",
          "Valspar Paint",
          "Build.com",
          "Cupcake Vineyards",
          "Freebies 4 Mom",
          "Sanus",
          "Womanista",
          "Purina Cat Chow",
          "Petcentric by Purina",
          "Einstein Bros Bagels",
          "Aunt Jemima Frozen Breakfast",
          "Rite Aid",
          "Derma E",
          "Thermos",
          "Funko",
          "Viactiv",
          "Nike Soccer",
          "Sara Lee Bread",
          "Vlasic Stork",
          "LeapFrog",
          "[yellow tail]",
          "BAILEYS Coffee Creamers",
          "UNU",
          "PowerBar",
          "Microsoft",
          "Freebie Mom",
          "Good Earth Tea",
          "SweetLeaf Stevia",
          "Healthy Essentials",
          "Rolaids",
          "ScotchBlue",
          "TUMI",
          "Eckrich",
          "Brookside Chocolate",
          "Dove Men+Care",
          "Hometalk",
          "GoGo squeeZ",
          "Bodyography Professional Cosmetics",
          "Hydroxycut",
          "Michelin",
          "Cate & Chloe",
          "AMZ Review Trader",
          "S&W Premium Beans",
          "Juicy Juice",
          "Royal Draw",
          "Emmi USA",
          "BrylaneHome.com",
          "Hostess",
          "Gillette",
          "Kraft Natural Cheese",
          "REAL CALIFORNIA MILK",
          "Tic Tac",
          "Igloo Coolers",
          "Earthbound Farm",
          "Lemonhead",
          "Nature Valley",
          "Academy Sports + Outdoors",
          "The Bradford Exchange",
          "Touch of Modern",
          "Ty",
          "BLACK+DECKER",
          "Shari's Berries",
          "Huggies",
          "DEWALT",
          "Pop-Tarts",
          "POND'S",
          "SKIPPY Peanut Butter",
          "NESCAFÉ",
          "Chicken of the Sea",
          "McCormick Grill Mates",
          "Lion Brand Yarn",
          "Love2Love Fragrances",
          "Jif",
          "Lever 2000",
          "Coca-Cola",
          "Lands' End",
          "Train",
          "Too Faced Cosmetics",
          "Bounty",
          "Sharp AQUOS",
          "12 Tomatoes",
          "Foot Locker",
          "Zapzyt",
          "Cheerios",
          "Pacific Foods",
          "Airborne Health",
          "Amazon Electronics",
          "DENIZEN",
          "Nature's Bounty",
          "Safeway",
          "Ball® Canning & Recipes",
          "Tony's Pizza",
          "Miracle Whip",
          "beso.com",
          "Yes We Coupon",
          "Ford Mustang",
          "Contigo",
          "Teleflora",
          "Irish Spring",
          "Crafts Direct",
          "Dietz & Watson",
          "eos",
          "NYDJ",
          "Jennie-O",
          "Morris The Cat",
          "Pure Leaf Iced Tea",
          "Rubbermaid",
          "Nesquik",
          "Dentyne",
          "Lancôme",
          "Seagram's Escapes",
          "Oikos Greek Yogurt",
          "Balance Bar",
          "Silk",
          "Nonni's Biscotti",
          "Hooters",
          "Scünci Hair Accessories",
          "Scotties Facial Tissue",
          "SodaStream USA",
          "Nordstrom",
          "Big Joe",
          "ACE",
          "DiGiorno",
          "Direct Eats",
          "Reddi-wip",
          "Can-Am Spyder",
          "GoSunoco",
          "DSW Designer Shoe Warehouse",
          "BIC Multi-Purpose Lighters",
          "Kaz USA, Inc.",
          "Ovaltine USA",
          "Wonderful Almonds",
          "Murad",
          "Original Cup Noodles",
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     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7456/76400081.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m     \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'_cramer_US'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     88\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# to plot figure inline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\chart_studio\\plotly\\plotly.py\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m\"auto_open\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"auto_open\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\chart_studio\\plotly\\plotly.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m    274\u001b[0m             \u001b[0mgrid_filename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"_grid\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 276\u001b[1;33m         grid_ops.upload(\n\u001b[0m\u001b[0;32m    277\u001b[0m             \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m             \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid_filename\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\chart_studio\\plotly\\plotly.py\u001b[0m in \u001b[0;36mupload\u001b[1;34m(cls, grid, filename, world_readable, auto_open, meta)\u001b[0m\n\u001b[0;32m   1085\u001b[0m                 \u001b[0mpayload\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"parent_path\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparent_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1086\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1087\u001b[1;33m         \u001b[0mfile_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_create_or_overwrite_grid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpayload\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1088\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1089\u001b[0m         \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"cols\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\chart_studio\\plotly\\plotly.py\u001b[0m in \u001b[0;36m_create_or_overwrite_grid\u001b[1;34m(data, max_retries)\u001b[0m\n\u001b[0;32m   1548\u001b[0m     \u001b[1;31m# Create file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1549\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1550\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapi_module\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1551\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1552\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmax_retries\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"already exists\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\chart_studio\\api\\v2\\grids.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(body)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \"\"\"\n\u001b[0;32m     17\u001b[0m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuild_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRESOURCE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"post\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m     47\u001b[0m             \u001b[1;33m@\u001b[0m\u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m             \u001b[1;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mRetrying\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_reject\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mattempt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mattempt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wrap_exception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m             \u001b[0mdelay_since_first_attempt_ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, wrap_exception)\u001b[0m\n\u001b[0;32m    245\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mRetryError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m                 \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\six.py\u001b[0m in \u001b[0;36mreraise\u001b[1;34m(tp, value, tb)\u001b[0m\n\u001b[0;32m    717\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 719\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    720\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\retrying.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m    198\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    199\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m                 \u001b[0mattempt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAttempt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattempt_number\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    201\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    202\u001b[0m                 \u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\chart_studio\\api\\v2\\utils.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mRequestException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m         \u001b[1;31m# The message can be an exception. E.g., MaxRetryError.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\api.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    540\u001b[0m         }\n\u001b[0;32m    541\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    653\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    654\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 655\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    656\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    657\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    437\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 439\u001b[1;33m                 resp = conn.urlopen(\n\u001b[0m\u001b[0;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    697\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    698\u001b[0m             \u001b[1;31m# Make the request on the httplib connection object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 699\u001b[1;33m             httplib_response = self._make_request(\n\u001b[0m\u001b[0;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    443\u001b[0m                     \u001b[1;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    444\u001b[0m                     \u001b[1;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 445\u001b[1;33m                     \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    446\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\packages\\six.py\u001b[0m in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    438\u001b[0m                 \u001b[1;31m# Python 3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m                     \u001b[0mhttplib_response\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m                     \u001b[1;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1369\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1370\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1371\u001b[1;33m                 \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1372\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1373\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    317\u001b[0m         \u001b[1;31m# read until we get a non-100 response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    318\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 319\u001b[1;33m             \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    320\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 280\u001b[1;33m         \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iso-8859-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    281\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    282\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"status line\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    702\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    703\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 704\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    705\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    706\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1239\u001b[0m                   \u001b[1;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1240\u001b[0m                   self.__class__)\n\u001b[1;32m-> 1241\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1242\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1243\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m   1097\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1098\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1099\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1100\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1101\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# plot ideology and homogeneity of pages in category using plotly (Figure SA5)\n",
    "categories = Cats['Category'].tolist() #specify category(/ies)\n",
    "x_col = 'page_ideo_corr'\n",
    "y_col = 'cramer_v'\n",
    "\n",
    "#pages_info_group = pages_info[pages_info['news2'] == 'hardnews'] #hardnews pages\n",
    "#pages_info_group = pages_info[pages_info['news_politics'] == 'y'] # partisan news pages\n",
    "    \n",
    "for cat in categories:\n",
    "    pages_info_group = pages_info[pages_info['category'].str.contains(cat)] # all other categories\n",
    "    \n",
    "    num=3 # how many names to print in figure\n",
    "    mid= round(len(pages_info_group[x_col])/2) # find middle of rows for moderate pages\n",
    "    gem = pages_info_group['page_count'].mean()\n",
    "    trace1 = go.Scatter(\n",
    "            x=pages_info_group[x_col], \n",
    "            y= pages_info_group[y_col],\n",
    "            mode='markers',\n",
    "            name='popular',\n",
    "            marker={\n",
    "                \"size\": list((pages_info_group['page_count']/gem)*10),\n",
    "                },\n",
    "            text= list(pages_info_group['page_name'])\n",
    "            )\n",
    "\n",
    "    trace2 = go.Scatter(\n",
    "            x=pages_info_group[x_col][:num], \n",
    "            y= pages_info_group[y_col][:num],\n",
    "            mode='text',\n",
    "            name='popular',\n",
    "            text= list(pages_info_group['page_name'][:num]),\n",
    "        textposition='middle left',\n",
    "            textfont=dict(\n",
    "                family='Computer Modern',\n",
    "                size=16,\n",
    "                color='#343A3D'\n",
    "            )) # print names for the 3 most liberal pages\n",
    "\n",
    "    trace3 = go.Scatter(\n",
    "            x=pages_info_group[x_col][mid-1:mid+2], \n",
    "            y= pages_info_group[y_col][mid-1:mid+2],\n",
    "            mode='text',\n",
    "            name='popular',\n",
    "            text= list(pages_info_group['page_name'][mid-1:mid+2]),\n",
    "        textposition='top center',\n",
    "            textfont=dict(\n",
    "                family='Computer Modern',\n",
    "                size=16,\n",
    "                color='#343A3D'\n",
    "            )) # print names for the 3 moderate pages\n",
    "    \n",
    "    trace4 = go.Scatter(\n",
    "            x=pages_info_group[x_col][-num:], \n",
    "            y= pages_info_group[y_col][-num:],\n",
    "            mode='text',\n",
    "            name='popular',\n",
    "            text= list(pages_info_group['page_name'][-num:]),\n",
    "        textposition='middle right',\n",
    "            textfont=dict(\n",
    "                family='Computer Modern',\n",
    "                size=16,\n",
    "                color='#343A3D'\n",
    "            )) # print names for the 3 most conservative pages\n",
    "\n",
    "\n",
    "    data = [trace1, trace2, trace3, trace4]\n",
    "\n",
    "    layout = go.Layout(\n",
    "        showlegend=False,\n",
    "        xaxis=dict(\n",
    "            title='ideology',\n",
    "            range=[0,1],\n",
    "            titlefont=dict(\n",
    "            family='Computer Modern',\n",
    "            size=12)\n",
    "            ),\n",
    "\n",
    "        yaxis=dict(\n",
    "            title='homogeneity',\n",
    "            range=[0,0.6],\n",
    "            titlefont=dict(\n",
    "            family='Computer Modern',\n",
    "            size=12)\n",
    "    ))\n",
    "\n",
    "    fig = go.Figure(data=data, layout=layout)\n",
    "    py.iplot(fig, filename=cat+'_cramer_US')\n",
    "    print(cat)\n",
    "    fig.show() # to plot figure inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2 Predictors for individual homogeneity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add number of likes per group to user info\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    group = user_likes2[(user_likes2['group'].str.contains(gr))]\n",
    "    num_likes_group = pd.DataFrame(group.resp_id.value_counts()).reset_index().rename(columns={'resp_id':'num_likes_'+gr,'index':'resp_id'})\n",
    "    user_info = user_info.merge(num_likes_group, on='resp_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add number of likes per group to user info\n",
    "# 70 percentile for liberals, 30 percentile for conservatives\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    perc = np.percentile(density[gr][0], 70)\n",
    "    group = user_likes2[(user_likes2['group'].str.contains(gr))&(user_likes2['page_ideo_corr']<perc)]\n",
    "    num_likes_group = pd.DataFrame(group.resp_id.value_counts()).reset_index().rename(columns={'resp_id':'num_likes_lib_70%_'+gr,'index':'resp_id'})\n",
    "    user_info = user_info.merge(num_likes_group, on='resp_id', how='left')\n",
    "    \n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    perc = np.percentile(density[gr][2], 30)\n",
    "    group = user_likes2[(user_likes2['group'].str.contains(gr))&(user_likes2['page_ideo_corr']>perc)]\n",
    "    num_likes_group = pd.DataFrame(group.resp_id.value_counts()).reset_index().rename(columns={'resp_id':'num_likes_con_70%_'+gr,'index':'resp_id'})\n",
    "    user_info = user_info.merge(num_likes_group, on='resp_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Table 4\n",
    "# Average number of page likes per ideology\n",
    "av_lib=[]\n",
    "av_con=[]\n",
    "av_mod=[]\n",
    "\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    av_lib.append(user_info[(user_info['resp_id'].isin(lib_users))]['num_likes_'+gr].mean())\n",
    "    av_con.append(user_info[(user_info['resp_id'].isin(con_users))]['num_likes_'+gr].mean())\n",
    "    av_mod.append(user_info[(user_info['resp_id'].isin(mod_users))]['num_likes_'+gr].mean())\n",
    "\n",
    "\n",
    "av_lib_con = pd.DataFrame()\n",
    "av_lib_con['av_lib']=av_lib\n",
    "av_lib_con['av_mod']=av_mod\n",
    "av_lib_con['av_con']=av_con\n",
    "\n",
    "av_lib_con = av_lib_con.T\n",
    "av_lib_con[\"all\"] = av_lib_con.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrr}\n",
      "\\toprule\n",
      "{} &      0 &      1 &      2 &       3 &     all \\\\\n",
      "\\midrule\n",
      "av\\_lib &  21.98 &  12.62 &   7.56 &  220.78 &  262.94 \\\\\n",
      "av\\_mod &  16.86 &  10.21 &   6.83 &  276.26 &  310.16 \\\\\n",
      "av\\_con &  39.45 &  21.22 &  11.30 &  240.95 &  312.93 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# table 4\n",
    "print(av_lib_con.round(decimals=2).to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>3</th>\n",
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       "      <th>av_lib</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>84.0</td>\n",
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       "    <tr>\n",
       "      <th>av_mod</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>89.0</td>\n",
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       "    <tr>\n",
       "      <th>av_con</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0    1    2     3\n",
       "av_lib   8.0  5.0  3.0  84.0\n",
       "av_mod   5.0  3.0  2.0  89.0\n",
       "av_con  13.0  7.0  4.0  77.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percentage of likes per group\n",
    "(av_lib_con[[0,1,2,3]].div(av_lib_con['all'], axis=0)*100).round(decimals=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "left Politics: 0.3191705882352942\n",
      "right Politics: 0.7752581395348837\n",
      "left Polnews: 0.4044021276595745\n",
      "right Polnews: 0.7550115577889447\n",
      "left Hardnews: 0.4651137931034483\n",
      "right Hardnews: 0.5910870967741935\n",
      "left Lifestyle: 0.5178461988304094\n",
      "right Lifestyle: 0.4923124031007752\n"
     ]
    }
   ],
   "source": [
    "#Table 5\n",
    "for group in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "\n",
    "    print(\"left \" +group+\": \" + str(np.percentile(density[group][0], 70)))\n",
    "    print(\"right \" +group+\": \" + str(np.percentile(density[group][2], 30)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# percentage of liberal users with more than 5% conservative page likes per group\n",
    "# percentage of conservative users with more than 5% liberal page likes per group\n",
    "perc_libcon=[]\n",
    "perc_liblib=[]\n",
    "perc_conlib=[]\n",
    "perc_concon=[]\n",
    "\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    lib_lib = user_info[(user_info['resp_id'].isin(lib_users))&(user_info['num_likes_lib_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    lib_con = user_info[(user_info['resp_id'].isin(lib_users))&(user_info['num_likes_con_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    perc_liblib.append(len(lib_lib)/len(lib_users))\n",
    "    perc_libcon.append(len(lib_con)/len(lib_users))\n",
    "    con_lib = user_info[(user_info['resp_id'].isin(con_users))&(user_info['num_likes_lib_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    con_con = user_info[(user_info['resp_id'].isin(con_users))&(user_info['num_likes_con_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    perc_conlib.append(len(con_lib)/len(con_users))\n",
    "    perc_concon.append(len(con_con)/len(con_users))\n",
    "    \n",
    "perc_lib_con = pd.DataFrame()\n",
    "perc_lib_con['Liberals with min. 5% liberal likes']=perc_liblib\n",
    "perc_lib_con['Liberals with min. 5% conservative likes']=perc_libcon\n",
    "perc_lib_con['Conservatives with min. 5% liberal likes']=perc_conlib\n",
    "perc_lib_con['Conservatives with min. 5% conservative likes']=perc_concon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "{} &  Liberals with min. 5\\% conservative likes &  Conservatives with min. 5\\% liberal likes \\\\\n",
      "\\midrule\n",
      "0 &                                      0.08 &                                      0.09 \\\\\n",
      "1 &                                      0.07 &                                      0.20 \\\\\n",
      "2 &                                      0.18 &                                      0.31 \\\\\n",
      "3 &                                      0.97 &                                      0.97 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Table 6\n",
    "perc_lib_con = pd.DataFrame()\n",
    "\n",
    "perc_lib_con['Liberals with min. 5% conservative likes']=perc_libcon\n",
    "perc_lib_con['Conservatives with min. 5% liberal likes']=perc_conlib\n",
    "\n",
    "print(perc_lib_con.round(decimals=2).to_latex())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    user_info['num_likes_' + gr] = user_info['num_likes_'+gr].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Table 7\n",
    "# Average number of likes of liberal users and of liberal users with more than 5% conservative page likes per group\n",
    "# Average number of likes of conservatives and of conservative users with more than 5% liberal page likes per group\n",
    "av_lib=[]\n",
    "av_libcon=[]\n",
    "av_con=[]\n",
    "av_conlib=[]\n",
    "std_lib=[]\n",
    "std_libcon=[]\n",
    "std_con=[]\n",
    "std_conlib=[]\n",
    "\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    lib_con = user_info[(user_info['resp_id'].isin(lib_users))&(user_info['num_likes_con_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    av_lib.append(user_info[(user_info['resp_id'].isin(lib_users))]['num_likes_Politics'].mean())\n",
    "    av_libcon.append(user_info[(user_info['resp_id'].isin(lib_con))]['num_likes_Politics'].mean())\n",
    "    std_lib.append(user_info[(user_info['resp_id'].isin(lib_users))]['num_likes_Politics'].std())\n",
    "    std_libcon.append(user_info[(user_info['resp_id'].isin(lib_con))]['num_likes_Politics'].std())\n",
    "    con_lib = user_info[(user_info['resp_id'].isin(con_users))&(user_info['num_likes_lib_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    av_con.append(user_info[(user_info['resp_id'].isin(con_users))]['num_likes_Politics'].mean())\n",
    "    av_conlib.append(user_info[(user_info['resp_id'].isin(con_lib))]['num_likes_Politics'].mean())\n",
    "    std_con.append(user_info[(user_info['resp_id'].isin(con_users))]['num_likes_Politics'].std())\n",
    "    std_conlib.append(user_info[(user_info['resp_id'].isin(con_lib))]['num_likes_Politics'].std())\n",
    "    \n",
    "av_lib_con = pd.DataFrame()\n",
    "av_lib_con['av_lib']=av_lib\n",
    "av_lib_con['av_libcon']=av_libcon\n",
    "av_lib_con['av_con']=av_con\n",
    "av_lib_con['av_conlib']=av_conlib\n",
    "av_lib_con['std_lib']=std_lib\n",
    "av_lib_con['std_libcon']=std_libcon\n",
    "av_lib_con['std_con']=std_con\n",
    "av_lib_con['std_conlib']=std_conlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "{} &  av\\_libcon &  av\\_conlib \\\\\n",
      "\\midrule\n",
      "0 &      27.83 &      26.32 \\\\\n",
      "1 &      33.12 &      53.13 \\\\\n",
      "2 &      39.06 &      54.19 \\\\\n",
      "3 &      19.13 &      30.97 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Table 7 \n",
    "av_lib_con = pd.DataFrame()\n",
    "av_lib_con['av_libcon']=av_libcon\n",
    "av_lib_con['av_conlib']=av_conlib\n",
    "\n",
    "print(av_lib_con.round(decimals=2).to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Two-tailed T-test for the means of two independent samples\n",
    "\n",
    "from scipy import stats\n",
    "p_lib=[]\n",
    "p_con=[]\n",
    "for gr in ['Politics','Polnews','Hardnews','Lifestyle']:\n",
    "    lib_con = user_info[(user_info['resp_id'].isin(lib_users))&(user_info['num_likes_con_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    t, p = stats.ttest_ind( user_info[(user_info['resp_id'].isin(lib_users))]['num_likes_Politics'] , user_info[(user_info['resp_id'].isin(lib_con))]['num_likes_Politics'] )\n",
    "    p_lib.append(p)\n",
    "    con_lib = user_info[(user_info['resp_id'].isin(con_users))&(user_info['num_likes_lib_70%_'+gr]/user_info['num_likes_'+gr]>0.05)]['resp_id'].unique()\n",
    "    t, p = stats.ttest_ind( user_info[(user_info['resp_id'].isin(con_users))]['num_likes_Politics'] , user_info[(user_info['resp_id'].isin(con_lib))]['num_likes_Politics'] )\n",
    "    p_con.append(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>liberals</th>\n",
       "      <th>conservatives</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.057</td>\n",
       "      <td>0.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.821</td>\n",
       "      <td>0.991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   liberals  conservatives\n",
       "0     0.057          0.727\n",
       "1     0.004          0.055\n",
       "2     0.000          0.015\n",
       "3     0.821          0.991"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# p-values t-test Table 7 \n",
    "p_val = pd.DataFrame()\n",
    "p_val['liberals'] = p_lib\n",
    "p_val['conservatives'] = p_con\n",
    "p_val.round(decimals=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Government & Politics: 353\n",
      "Polnews: 188\n",
      "Hardnews: 103\n",
      "Lifestyle: 4667\n"
     ]
    }
   ],
   "source": [
    "# calculate average cramer v per user per category (Figure 7)\n",
    "def user_score(group, var, cat):\n",
    "    user_var = pd.DataFrame(group.groupby(['resp_id'])[var].mean())\n",
    "    user_var.rename(columns={var:cat}, inplace=True)\n",
    "    user_var.reset_index(inplace=True)\n",
    "    return user_var\n",
    "\n",
    "var = 'cramer_v'\n",
    "#user_info = pd.DataFrame(user_ids, columns={'resp_id'})\n",
    "\n",
    "#government and politics \n",
    "group = user_likes2[(user_likes2['group'].str.contains('Politics'))]\n",
    "print('Government & Politics: ' + str(group['page_name'].nunique()))\n",
    "user_var = user_score(group, var, 'cramer_v_politics')\n",
    "user_info = user_info.merge(user_var, on='resp_id', how='left')\n",
    "\n",
    "# Polnews\n",
    "group = user_likes2[(user_likes2['group'].str.contains('Polnews'))]\n",
    "print('Polnews: ' + str(group['page_name'].nunique()))\n",
    "user_var = user_score(group, var, 'cramer_v_polnews')\n",
    "user_info = user_info.merge(user_var, on='resp_id', how='left')\n",
    "\n",
    "# Hardnews\n",
    "group = user_likes2[(user_likes2['group'].str.contains('Hardnews'))]\n",
    "print('Hardnews: ' + str(group['page_name'].nunique()))\n",
    "user_var = user_score(group, var, 'cramer_v_hardnews')\n",
    "user_info = user_info.merge(user_var, on='resp_id', how='left')\n",
    "\n",
    "# lifestyle\n",
    "group = user_likes2[(user_likes2['group'].str.contains('Lifestyle'))]\n",
    "print('Lifestyle: ' + str(group['page_name'].nunique()))\n",
    "user_var = user_score(group, var, 'cramer_v_lifestyle')\n",
    "user_info = user_info.merge(user_var, on='resp_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib_users = user_info[user_info['ideo5']<3]\n",
    "mod_users = user_info[user_info['ideo5']==3]\n",
    "con_users = user_info[user_info['ideo5'].isin([4,5])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n",
      "C:\\Users\\stien\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning:\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Individual homogeneity distribution (Figure 7)\n",
    "df = pd.DataFrame()\n",
    "\n",
    "for variable in ['cramer_v_politics','cramer_v_polnews', 'cramer_v_hardnews','cramer_v_lifestyle']:\n",
    "#for variable in ['ideo_corr_wavg_30', 'ideo_corr_wavg_all']:\n",
    "    plt.figure()\n",
    "    sns.distplot(lib_users[variable], hist=False, kde_kws = {'shade': True, 'linewidth': 1}, color='b', kde=True)\n",
    "    sns.distplot(mod_users[variable], hist=False, kde_kws = {'shade': True, 'linewidth': 1}, color='purple',  kde=True)\n",
    "    sns.distplot(con_users[variable], hist=False, kde_kws = {'shade': True, 'linewidth': 1}, color='r',  kde=True)\n",
    "    plt.xlabel('Homogeneity score')\n",
    "    plt.ylabel('Density')\n",
    "    plt.xlim(0,0.6)\n",
    "    plt.ylim(0,25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors_ideo = ['blue' if x in [1,2] else 'red' if x in [4,5] else 'purple' if x ==3 else 'grey' for x in user_info['ideo5']]\n",
    "#colors_pid = ['blue' if x ==1 else 'red' if x == 2 else 'purple' if x in [3,4] else 'grey' for x in user_info['pid3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"525px\"\n",
       "            src=\"https://plotly.com/~stiene.praet/89.embed\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1bb04a43220>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot ideology and homogeneity for users (Figure SA6)\n",
    "\n",
    "group = user_info\n",
    "colors = colors_ideo # define colors\n",
    "minn = group['user_count'].min()\n",
    "maxx = user_info['user_count'].max()\n",
    "\n",
    "#for cat in ['Government & Politics','Political news','Hardnews','Lifestyle']:\n",
    "trace1 = go.Scatter(\n",
    "        x=user_info['resp_page_ideo_corr'].tolist(), \n",
    "        y= user_info['resp_cramer_v'].tolist(),\n",
    "        mode='markers',\n",
    "        name='popular',\n",
    "        marker={\n",
    "            \"size\": list((((user_info['user_count']-minn)/(maxx-minn))*20)+10),\n",
    "            \"color\": colors\n",
    "            },\n",
    "        )\n",
    "\n",
    "\n",
    "data = [trace1]\n",
    "\n",
    "layout = go.Layout(\n",
    "    template=\"plotly_white\",\n",
    "    showlegend=False,\n",
    "    xaxis=dict(\n",
    "        title='ideology',\n",
    "        range=[0,1.1],\n",
    "        titlefont=dict(\n",
    "        family='Computer Modern',\n",
    "        size=12)\n",
    "        ),\n",
    "\n",
    "    yaxis=dict(\n",
    "        title='homogeneity',\n",
    "        titlefont=dict(\n",
    "        family='Computer Modern',\n",
    "        size=12),\n",
    "        range=[0,0.6]\n",
    "))\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='us_users')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_info.to_csv('../data/user_info.csv', index=False) # save as csv for beta regressions in R"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
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